Random walks with shape prior for cochlea segmentation in ex vivo μCT

Esmeralda Ruiz Pujadas, Hans Martin Kjer, Gemma Piella, Mario Ceresa, Miguel Angel González Ballester

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Purpose
Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we previously proposed the use of a high-resolution model built from μCT images and then adapted to patient-specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate μCT
segmentation algorithms.

Methods
We propose a new framework for cochlea segmentation in ex vivo μCT images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration.

Results
We tested the proposed approach in ten μCT data sets and compared it with other random walks-based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17(2):236–253, 2013) and constrained random walks (Li et al. in Advances in image and video technology. Springer, Berlin, pp 215–226, 2012). Our approach demonstrated higher accuracy results due to the probability density model constituted by the region term and shape prior information weighed by a confidence map.

Conclusion
The weighted combination of the distance-based shape prior with a region term into random walks provides accurate segmentations of the cochlea. The experiments suggest that the proposed approach is robust for cochlea segmentation.
Original languageEnglish
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume11
Issue number9
Pages (from-to)1647-1659
ISSN1861-6410
DOIs
Publication statusPublished - 2016

Keywords

  • Random walks
  • Random walks with prior
  • Shape prior
  • Prior models
  • Distance map
  • Probabilistic map

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